% Use this file for citations not found in the ACL Anthology (contained in "anthology.bib").
@article{zhao2023survey,
  title={A Survey of Large Language Models},
  author={Zhao, Wayne Xin and Zhou, Kun and Li, Junyi and Tang, Tianyi and Wang, Xiaolei and Hou, Yupeng and Min, Yingqian and Zhang, Beichen and Zhang, Junjie and Dong, Zican and others},
  journal={arXiv preprint arXiv:2303.18223},
  year={2023}
}

@article{brown2020language,
  title={Language models are few-shot learners},
  author={Brown, Tom and Mann, Benjamin and Ryder, Nick and Subbiah, Melanie and Kaplan, Jared D and Dhariwal, Prafulla and Neelakantan, Arvind and Shyam, Pranav and Sastry, Girish and Askell, Amanda and others},
  journal={Advances in neural information processing systems},
  volume={33},
  pages={1877--1901},
  year={2020}
}

@inproceedings{mihaylov2018can,
  title={Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answering},
  author={Mihaylov, Todor and Clark, Peter and Khot, Tushar and Sabharwal, Ashish},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  pages={2381--2391},
  year={2018}
}

@inproceedings{lin2022truthfulqa,
  title={TruthfulQA: Measuring How Models Mimic Human Falsehoods},
  author={Lin, Stephanie and Hilton, Jacob and Evans, Owain},
  booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={3214--3252},
  year={2022}
}

@article{manakul2023selfcheckgpt,
  title={SelfCheckGPT: Zero-Resource Black-Box Hallucination Detection for Generative Large Language Models},
  author={Manakul, Potsawee and Liusie, Adian and Gales, Mark JF},
  journal={arXiv preprint arXiv:2303.08896},
  year={2023}
}

@article{bang2023multitask,
  title={A multitask, multilingual, multimodal evaluation of chatgpt on reasoning, hallucination, and interactivity},
  author={Bang, Yejin and Cahyawijaya, Samuel and Lee, Nayeon and Dai, Wenliang and Su, Dan and Wilie, Bryan and Lovenia, Holy and Ji, Ziwei and Yu, Tiezheng and Chung, Willy and others},
  journal={arXiv preprint arXiv:2302.04023},
  year={2023}
}

@inproceedings{weidinger2022taxonomy,
  title={Taxonomy of risks posed by language models},
  author={Weidinger, Laura and Uesato, Jonathan and Rauh, Maribeth and Griffin, Conor and Huang, Po-Sen and Mellor, John and Glaese, Amelia and Cheng, Myra and Balle, Borja and Kasirzadeh, Atoosa and others},
  booktitle={2022 ACM Conference on Fairness, Accountability, and Transparency},
  pages={214--229},
  year={2022}
}

@article{choudhary2021linguistic,
  title={Linguistic feature based learning model for fake news detection and classification},
  author={Choudhary, Anshika and Arora, Anuja},
  journal={Expert Systems with Applications},
  volume={169},
  pages={114171},
  year={2021},
  publisher={Elsevier}
}

@inproceedings{jindal2020newsbag,
  title={Newsbag: A multimodal benchmark dataset for fake news detection},
  author={Jindal, Sarthak and Sood, Raghav and Singh, Richa and Vatsa, Mayank and Chakraborty, Tanmoy},
  booktitle={CEUR Workshop Proc.},
  volume={2560},
  pages={138--145},
  year={2020}
}

@article{zervopoulos2022deep,
  title={Deep learning for fake news detection on Twitter regarding the 2019 Hong Kong protests},
  author={Zervopoulos, Alexandros and Alvanou, Aikaterini Georgia and Bezas, Konstantinos and Papamichail, Asterios and Maragoudakis, Manolis and Kermanidis, Katia},
  journal={Neural Computing and Applications},
  volume={34},
  number={2},
  pages={969--982},
  year={2022},
  publisher={Springer}
}

@article{han2021pre,
  title={Pre-trained models: Past, present and future},
  author={Han, Xu and Zhang, Zhengyan and Ding, Ning and Gu, Yuxian and Liu, Xiao and Huo, Yuqi and Qiu, Jiezhong and Yao, Yuan and Zhang, Ao and Zhang, Liang and others},
  journal={AI Open},
  volume={2},
  pages={225--250},
  year={2021},
  publisher={Elsevier}
}

@article{dasgupta2022language,
  title={Language models show human-like content effects on reasoning},
  author={Dasgupta, Ishita and Lampinen, Andrew K and Chan, Stephanie CY and Creswell, Antonia and Kumaran, Dharshan and McClelland, James L and Hill, Felix},
  journal={arXiv preprint arXiv:2207.07051},
  year={2022}
}

@article{shiffrin2023probing,
  title={Probing the psychology of AI models},
  author={Shiffrin, Richard and Mitchell, Melanie},
  journal={Proceedings of the National Academy of Sciences},
  volume={120},
  number={10},
  pages={e2300963120},
  year={2023},
  publisher={National Acad Sciences}
}

@inproceedings{chen2017reading,
  title={Reading Wikipedia to Answer Open-Domain Questions},
  author={Chen, Danqi and Fisch, Adam and Weston, Jason and Bordes, Antoine},
  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={1870--1879},
  year={2017}
}

@article{etezadi2023state,
  title={The state of the art in open domain complex question answering: a survey},
  author={Etezadi, Romina and Shamsfard, Mehrnoush},
  journal={Applied Intelligence},
  volume={53},
  number={4},
  pages={4124--4144},
  year={2023},
  publisher={Springer}
}

@inproceedings{chen2020open,
  title={Open-domain question answering},
  author={Chen, Danqi and Yih, Wen-tau},
  booktitle={Proceedings of the 58th annual meeting of the association for computational linguistics: tutorial abstracts},
  pages={34--37},
  year={2020}
}

@article{albahar2021hybrid,
  title={A hybrid model for fake news detection: Leveraging news content and user comments in fake news},
  author={Albahar, Marwan},
  journal={IET Information Security},
  volume={15},
  number={2},
  pages={169--177},
  year={2021},
  publisher={Wiley Online Library}
}

@article{mialon2023augmented,
  title={Augmented language models: a survey},
  author={Mialon, Gr{\'e}goire and Dess{\`\i}, Roberto and Lomeli, Maria and Nalmpantis, Christoforos and Pasunuru, Ram and Raileanu, Roberta and Rozi{\`e}re, Baptiste and Schick, Timo and Dwivedi-Yu, Jane and Celikyilmaz, Asli and others},
  journal={arXiv preprint arXiv:2302.07842},
  year={2023}
}

@inproceedings{welleckneural,
  title={Neural Text Generation With Unlikelihood Training},
  author={Welleck, Sean and Kulikov, Ilia and Roller, Stephen and Dinan, Emily and Cho, Kyunghyun and Weston, Jason},
  booktitle={International Conference on Learning Representations}
}

@inproceedings{yang2018hotpotqa,
  title={HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering},
  author={Yang, Zhilin and Qi, Peng and Zhang, Saizheng and Bengio, Yoshua and Cohen, William and Salakhutdinov, Ruslan and Manning, Christopher D},
  booktitle={Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing},
  pages={2369--2380},
  year={2018}
}

@inproceedings{joshi2017triviaqa,
  title={TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension},
  author={Joshi, Mandar and Choi, Eunsol and Weld, Daniel S and Zettlemoyer, Luke},
  booktitle={Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  pages={1601--1611},
  year={2017}
}

@inproceedings{ho2020constructing,
  title={Constructing A Multi-hop QA Dataset for Comprehensive Evaluation of Reasoning Steps},
  author={Ho, Xanh and Nguyen, Anh-Khoa Duong and Sugawara, Saku and Aizawa, Akiko},
  booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
  pages={6609--6625},
  year={2020}
}

@inproceedings{otegi-eta-2020-automatic,
    title = "Automatic Evaluation vs. User Preference in Neural Textual {Q}uestion{A}nswering over {COVID}-19 Scientific Literature",
    author = "Otegi, Arantxa  and
      Campos, Jon Ander  and
      Azkune, Gorka  and
      Soroa, Aitor  and
      Agirre, Eneko",
    booktitle = "Proceedings of the 1st Workshop on {NLP} for {COVID}-19 (Part 2) at {EMNLP} 2020",
    month = dec,
    year = "2020",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2020.nlpcovid19-2.15",
    doi = "10.18653/v1/2020.nlpcovid19-2.15",
    abstract = "We present a Question Answering (QA) system that won one of the tasks of the Kaggle CORD-19 Challenge, according to the qualitative evaluation of experts. The system is a combination of an Information Retrieval module and a reading comprehension module that finds the answers in the retrieved passages. In this paper we present a quantitative and qualitative analysis of the system. The quantitative evaluation using manually annotated datasets contradicted some of our design choices, e.g. the fact that using QuAC for fine-tuning provided better answers over just using SQuAD. We analyzed this mismatch with an additional A/B test which showed that the system using QuAC was indeed preferred by users, confirming our intuition. Our analysis puts in question the suitability of automatic metrics and its correlation to user preferences. We also show that automatic metrics are highly dependent on the characteristics of the gold standard, such as the average length of the answers.",
}